CryptoTimeSeriesProject/Auto_Time_Series_Functions.py at main · elayer/CryptoTimeSeriesProject · GitHub

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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()

import time
from datetime import datetime
from datetime import date
from datetime import timedelta

import yfinance
from yahoo_fin.stock_info import get_data

def get_close_column(crypto_dict):

    """
    This function returns the closing prices for each chosen cryptocurrency ticker

    args:
        crypto_dict (dict): The dictionary containing all cryptocurrency data returned from Yahoo! finance

    returns:
        data (DataFrame): A dataframe containing only the closing prices of the chosen cryptocurrencies

    """
    keys = crypto_dict.keys()

    data = pd.DataFrame()

    for key in keys:
        data[key+'_Close'] = crypto_dict[key]['close']

    return data


def get_volume_column(crypto_dict):

    """
    This function returns the volume for each chosen cryptocurrency ticker

    args:
        crypto_dict (dict): The dictionary containing all cryptocurrency data returned from Yahoo! finance

    returns:
        data (DataFrame): A dataframe containing only the volumes of the chosen cryptocurrencies

    """
    keys = crypto_dict.keys()

    data = pd.DataFrame()

    for key in keys:
        data[key+'_Volume'] = crypto_dict[key]['volume']

    return data

def auto_create_ticker_table(symbols, start_date="2017-01-01", interval = '1d'):

    """
    This function creates a table that contains closing prices and estimated returns for choice cryptocurrencies
    to be used for analysis, plotting, forecasting, etc.

    args:
        symbols (list): Tickers to use in creating models and analyzing
        tickers (list): A default list containing the choice cryptocurrencies to evaluate
        start_date (date): The beginning date to start the table from
        interval (str): The time interval to construct the table from

    returns:
        df (DataFrame): The data table containing the data of cryptocurrency closing prices and returns

    """

    tickers = ['BTC-USD', 'ETH-USD', 'USDT-USD', 'BNB-USD', 'USDC-USD', 'HEX-USD', 'XRP-USD', 'SOL-USD', 'ADA-USD', 'BUSD-USD', 'UST-USD', 'DOGE-USD', 'AVAX-USD', 'DOT-USD', 'SHIB-USD', 'WBTC-USD', 'LUNA1-USD', 'WTRX-USD', 'STETH-USD', 'TRX-USD', 'DAI-USD', 'MATIC-USD', 'NEAR-USD', 'CRO-USD', 'LTC-USD']

    today = date.today()
    prev_day = pd.to_datetime(today - timedelta(days=1)).strftime("%Y-%m-%d")

    stopper = True
    num_closes = 0
    #print('Note: Bitcoin is already in the table by default') #In order to make the function diverse, it has been removed

    #while stopper:
        #crypto_str = input('Enter a ticker from Yahoo! Finance. Enter "No" to continue: ')
        #if crypto_str.upper() == 'NO':
            #break
            #stopper = False
        #else:
		#num_closes += 1
            #tickers.append(crypto_str)


    crypto_data = {}

    #rate = input("Do you want daily or business day data? Enter 'd' for daily and 'b' for business: ")
    #rate = rate.lower() #Using lower method for assurance

    #while rate != 'b' and rate != 'd':
        #rate = input("Please enter again: ")
        #rate = rate.lower()

    rate = 'b'

    for ticker in symbols:
        crypto_data[ticker] = get_data(ticker, start_date = start_date, end_date = prev_day, index_as_date = True, interval = interval)

    closes = get_close_column(crypto_data)
    volumes = get_volume_column(crypto_data)

    df_crypto = pd.concat([closes, volumes], axis=1)

    close_cols_width = closes.shape[1]

    df_crypto = df_crypto.iloc[1:]
    df_crypto = df_crypto.asfreq(rate) #User may decide the rate between d and b
    df_crypto = df_crypto.fillna(method='ffill')

    types = closes.columns.tolist()

    #squared returns = volatility

    for tick in types:
        df_crypto[tick.split('_')[0]+'_Return'] = df_crypto[tick].pct_change(1).mul(100)

        #Squared Returns:
        #df_crypto['Squared_'+tick.split(':')[0]+'_Return'] = df_crypto[tick.split(':')[0]+'_Return'].mul(df_crypto[tick.split(':')[0]+'_Return'])

    null_elim = df_crypto.iloc[:,:close_cols_width].notna().idxmax().max()

    trunc_df_crypto = df_crypto.loc[null_elim:, :].copy()
    trunc_df_crypto.fillna(0.0, inplace=True)

    print()
    print('********** Table Head **********')
    print(trunc_df_crypto.head(15))
    print('********** Table Tail **********')
    print(trunc_df_crypto.tail(15))

    return trunc_df_crypto